Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection

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Abstract
The need for precise identification of underwater sonar image targets is growing in areas such as marine resource exploitation, subsea construction, and ocean ecosystem surveillance. Nevertheless, conventional image recognition algorithms encounter several obstacles, including intricate underwater settings, poor-quality sonar image data, and limited sample quantities, which hinder accurate identification. This study seeks to improve underwater sonar image target recognition capabilities by employing deep learning techniques and developing the Multi-Gradient Feature Fusion YOLOv7 model (MFF-YOLOv7) to address these challenges. This model incorporates the Multi-Scale Information Fusion Module (MIFM) as a replacement for YOLOv7’s SPPCSPC, substitutes the Conv of CBS following ELAN with RFAConv, and integrates the SCSA mechanism at three junctions where the backbone links to the head, enhancing target recognition accuracy. Trials were conducted using datasets like URPC, SCTD, and UATD, encompassing comparative studies of attention mechanisms, ablation tests, and evaluations against other leading algorithms. The findings indicate that the MFF-YOLOv7 model substantially surpasses other models across various metrics, demonstrates superior underwater target detection capabilities, exhibits enhanced generalization potential, and offers a more dependable and precise solution for underwater target identification.
Funding Information
  • Guangxi Science and Technology Base and Talent Project (GuikeAD21220098, CRKL210102, Bei ke He 2023158004, YCSW2024344, 2024YCXS022, 2024YCXS033, 2023YCXS038)
  • the 2021 Open Fund project of the Key Laboratory of Cognitive Radio and Information Processing of the Ministry of Education (GuikeAD21220098, CRKL210102, Bei ke He 2023158004, YCSW2024344, 2024YCXS022, 2024YCXS033, 2023YCXS038)
  • Beihai City Science and Technology Bureau Project (GuikeAD21220098, CRKL210102, Bei ke He 2023158004, YCSW2024344, 2024YCXS022, 2024YCXS033, 2023YCXS038)
  • the Innovation Project of Guangxi Graduate Education (GuikeAD21220098, CRKL210102, Bei ke He 2023158004, YCSW2024344, 2024YCXS022, 2024YCXS033, 2023YCXS038)
  • the Innovation Project of GUET Graduate Education (GuikeAD21220098, CRKL210102, Bei ke He 2023158004, YCSW2024344, 2024YCXS022, 2024YCXS033, 2023YCXS038)

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